Digital images can be represented with a wide variety of color spaces and color encodings. The color gamut of an imaging system is the range of colors that can be represented or produced. An extended color gamut digital image has color values that are outside the limited color gamut of a storage color space. The process of mapping the colors of an extended color gamut digital image to the colors of a storage color space is often referred to as gamut mapping. Regardless of the gamut mapping strategy, there will always be a loss of information and/or a distortion of the color characteristics of the image.
Scene luminance dynamic range can be as large as 1000:1 or more, while most output devices (such as a video display or photographic print) cannot exceed a luminance dynamic range of 150:1 or so. A digital image made up of pixels having color values that represent scene color intensities is an extended color gamut digital image. By mapping the color values of such an image to the limited color gamut of an output device, a great deal of information is lost. For example, the gamut mapping procedure of mapping colors of an extended dynamic range digital image to the video “sRGB” color space generally discards a great deal of information, often due to the large difference in the luminance dynamic ranges. The process of using gamut mapping to map the colors of an extended color gamut digital image to the color gamut of a storage color space representing an output device (for example sRGB color space) is called rendering.
Rendered digital images (also called limited color gamut digital images) are generally very convenient for direct display on an output device. For example, limited color gamut digital images encoded in the sRGB color space look good on a video monitor. However, the information loss associated with the rendering process that was used to create the limited color gamut digital image makes it difficult to manipulate the image.
Rendered digital images can suffer from traditional photographic problems. These problems can be caused by things such as exposure error, colored illuminant, contrast variability, and the like. These problems are very difficult to fix with direct manipulation of the rendered digital image data. For example, the application of a linear function to the pixel values of a rendered image in the sRGB color space appears to modify the exposure, color saturation, and contrast of the image. Thus, it is difficult to correct or enhance a limited color gamut digital image.
However, the information in the extended color gamut digital image is useful for easily modifying the digital image. For example, when the colors of the extended color gamut digital image are related to the logarithm of the scene exposure, then exposure adjustments can be made simply by adding a constant to each color component value in the image. Contrast problems can often be corrected by applying a linear or nonlinear function to the image data.
Therefore, one method of applying modifications to a limited color gamut digital image includes the steps of:
a) creating a reconstructed extended color gamut digital image from the limited color gamut digital image;
b) enhancing the reconstructed extended color gamut digital image with an image modification step; and
c) creating an enhanced limited color gamut digital image by rendering the modified reconstructed extended color gamut digital image.
Steps b) and c) are both commonly known in the art of image processing. Several authors have described methods to accomplish step a). A successful method of creating a reconstructed extended color gamut digital image from a limited color gamut digital image must overcome several obstacles. First, the rendering process used to create a limited color gamut digital image from an extended color gamut digital image involves a loss of information, as previously described. Second, it is common to represent the limited color gamut digital image with only 8 bits per color channel of each pixel. This quantization, coupled with the gamut mapping, makes it very difficult to accurately create a reconstructed extended color gamut digital image. The process of creating a reconstructed extended dynamic range digital image from a limited color gamut digital image is referred to as “derendering”.
Spaulding et al. in U.S. Pat. No. 6,285,784 describe a method of applying manipulations to a limited color gamut digital image. In the described method, a limited color gamut digital image is combined with a residual image to form a reconstructed extended color gamut digital image. This reconstructed extended color gamut digital image is enhanced with modifications such as color balance adjustment or tone scale adjustment. Spaulding et al.'s method describes an effective method for applying a modification to a limited color gamut digital image by making an extended color gamut digital image. Unfortunately, Spaulding et al.'s method does not describe improving a limited color gamut digital image not having an associated residual image.
In U.S. Pat. No. 6,335,983, McCarthy et al. describe a derendering process by applying an inverse color adjustment function to a source digital image. This method can be used to generate an extended color gamut digital image from a limited color gamut digital image for the purpose of enhancing the image. The de-rendered image, the extended color gamut digital image, can then be improved with image modifications. However, their resulting image (the reconstructed extended color gamut digital image) can occasionally contain objectionable artifacts such as color contouring and quantization artifacts.